In [ ]:
!pip show geopandas
Name: geopandas Version: 1.0.1 Summary: Geographic pandas extensions Home-page: Author: Author-email: Kelsey Jordahl <kjordahl@alum.mit.edu> License: BSD 3-Clause Location: /usr/local/lib/python3.10/dist-packages Requires: numpy, packaging, pandas, pyogrio, pyproj, shapely Required-by: bigframes
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pip install fiona
Collecting fiona
Downloading fiona-1.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (56 kB)
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Requirement already satisfied: attrs>=19.2.0 in /usr/local/lib/python3.10/dist-packages (from fiona) (24.2.0)
Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from fiona) (2024.8.30)
Requirement already satisfied: click~=8.0 in /usr/local/lib/python3.10/dist-packages (from fiona) (8.1.7)
Collecting click-plugins>=1.0 (from fiona)
Downloading click_plugins-1.1.1-py2.py3-none-any.whl.metadata (6.4 kB)
Collecting cligj>=0.5 (from fiona)
Downloading cligj-0.7.2-py3-none-any.whl.metadata (5.0 kB)
Downloading fiona-1.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB)
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Downloading click_plugins-1.1.1-py2.py3-none-any.whl (7.5 kB)
Downloading cligj-0.7.2-py3-none-any.whl (7.1 kB)
Installing collected packages: cligj, click-plugins, fiona
Successfully installed click-plugins-1.1.1 cligj-0.7.2 fiona-1.10.1
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linkWorldMap="https://github.com/Halabbi/introgeodf2/raw/refs/heads/main/worldMaps%20.gpkg"
import geopandas as gpd
from fiona import listlayers
listlayers(linkWorldMap)
Out[ ]:
['countries', 'rivers', 'cities']
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airports_Russia="https://github.com/Halabbi/introgeodf2/raw/refs/heads/main/airports%20(1).gpkg"
import geopandas as gpd
from fiona import listlayers
listlayers(airports_Russia)
Out[ ]:
['rivers', 'airports_6078']
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countries=gpd.read_file(linkWorldMap,layer='countries')
rivers=gpd.read_file(linkWorldMap,layer='rivers')
cities=gpd.read_file(linkWorldMap,layer='cities')
airports_Rusia=gpd.read_file(airports_Russia,layer='airports_6078')
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#Seleccionamos el país y encontramos su centroide
rusia=countries[countries.COUNTRY=='Russia']
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#Seleccionamos el país
rusia=countries[countries.COUNTRY=='Russia']
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rusia_6078=rusia.to_crs(6078)
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rusia_6078.head()
Out[ ]:
| COUNTRY | geometry | |
|---|---|---|
| 188 | Russia | MULTIPOLYGON (((15109031.538 3917547.292, 1510... |
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#Centroide de Rusia
centroid=rusia_6078.centroid
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citiesRusia_clipped= gpd.clip(gdf=cities.to_crs(6078),
mask=rusia_6078)
riversRusia_clipped = gpd.clip(gdf=rivers.to_crs(6078),
mask=rusia_6078)
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#Los límites de Rusia
rusia_6078.total_bounds
Out[ ]:
array([11807370.64217146, 95446.02561431, 20250312.29794789,
5485536.46651407])
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#Los límites de los ríos de Rusia
riversRusia_clipped.total_bounds
Out[ ]:
array([12158367.80467051, 163504.84222059, 19217871.01839928,
4228894.90930308])
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riversRusia_clipped.cx[:, :].plot()
Out[ ]:
<Axes: >
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#Los límites de los aeropuertos de Rusia
airports_Rusia.total_bounds
Out[ ]:
array([11839498.85218068, 124114.13314903, 20258831.95747615,
5523188.92631749])
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#Los límites del centroide de Rusia
centroid.total_bounds
Out[ ]:
array([16052701.15946091, 2148267.30328144, 16052701.15946091,
2148267.30328144])
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#Los límites del las ciudades de Rusia
citiesRusia_clipped.total_bounds
Out[ ]:
array([12179490.07564848, 615451.79925766, 19725723.21361826,
4893441.78948158])
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citiesRusia_clipped.cx[:, :].plot()
Out[ ]:
<Axes: >
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# Se obtiene los límites máximos y mínimos de las ciudades
minx, miny, maxx, maxy = citiesRusia_clipped.total_bounds
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# Se obtiene los límites máximos y mínimos de los aeropuertos
minx, miny, maxx, maxy = airports_Rusia.total_bounds
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#Se obtiene los límites máximos y mínimos de los ríos
minx, miny, maxx, maxy = riversRusia_clipped.total_bounds
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# Base del mapa
base = rusia_6078.plot(facecolor="greenyellow", edgecolor='black', linewidth=0.4, figsize=(9,5))
# Centróide
rusia_6078.centroid.plot(color='black', markersize=5, ax=base)
# Ciudades de Rusia por debajo del centroide (coordenadas Y menores que 2148267.30328144)
citiesRusia_clipped.cx[minx:19725723.21361826, miny:2148267.30328144].plot(
marker='+', color='red', markersize=15, ax=base)
Out[ ]:
<Axes: >
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# Base del mapa
base = rusia_6078.plot(facecolor="greenyellow", edgecolor='black', linewidth=0.4, figsize=(9,5))
# Centróide
rusia_6078.centroid.plot(color='black', markersize=5, ax=base)
#Aeropuertos de Rusia por debajo del centroide (coordenadas y menores que 2148267.30328144)
airports_Rusia.cx[minx:20258831.95747615, miny:2148267.30328144].plot(
marker='+', color='orange', markersize=15, ax=base)
Out[ ]:
<Axes: >
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# Ciudades y aeropuertos debajo del centroide
base = rusia_6078.plot(facecolor="greenyellow", edgecolor='black', linewidth=0.4, figsize=(9,5))
rusia_6078.centroid.plot(color='black', markersize=5, ax=base)
airports_Rusia.cx[minx:20258831.95747615, miny:2148267.30328144].plot(
marker='+', color='orange', markersize=15, ax=base)
citiesRusia_clipped.cx[minx:19725723.21361826, miny:2148267.30328144].plot(
marker='+', color='red', markersize=15, ax=base)
Out[ ]:
<Axes: >
In [ ]:
# Base del mapa
base = rusia_6078.plot(facecolor="greenyellow", edgecolor='black', linewidth=0.4, figsize=(9,5))
# Centróide
rusia_6078.centroid.plot(color='black', markersize=5, ax=base)
# Ciudades por encima del centroide (coordenadas Y mayores que 2148267.30328144)
citiesRusia_clipped.cx[minx:19725723.21361826 , maxy: 2148267.30328144].plot(
marker='+', color='red', markersize=15, ax=base)
Out[ ]:
<Axes: >
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# Base del mapa
base = rusia_6078.plot(facecolor="greenyellow", edgecolor='black', linewidth=0.4, figsize=(9,5))
# Centróide
rusia_6078.centroid.plot(color='black', markersize=5, ax=base)
#Aeropuertos de Rusia por encima del centroide (coordenadas y menores que 2148267.30328144)
airports_Rusia.cx[minx:20258831.95747615, maxy:2148267.30328144].plot(
marker='+', color='orange', markersize=15, ax=base)
Out[ ]:
<Axes: >
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#Ciudades y aeropuertos por encima del centroide
base = rusia_6078.plot(facecolor="greenyellow", edgecolor='black', linewidth=0.4, figsize=(9,5))
rusia_6078.centroid.plot(color='black', markersize=5, ax=base)
airports_Rusia.cx[minx:20258831.95747615, maxy:2148267.30328144].plot(
marker='+', color='orange', markersize=15, ax=base)
citiesRusia_clipped.cx[minx:19725723.21361826 , maxy: 2148267.30328144].plot(
marker='+', color='red', markersize=15, ax=base)
Out[ ]:
<Axes: >
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#Ríos que cruzan esas divisiones
base = rusia_6078.plot(facecolor="greenyellow", edgecolor='black', linewidth=0.4, figsize=(9,5))
rusia_6078.centroid.plot(color='black', markersize=5, ax=base)
airports_Rusia.cx[minx:20258831.95747615, maxy:2148267.30328144].plot(
marker='+', color='orange', markersize=15, ax=base)
citiesRusia_clipped.cx[minx:19725723.21361826 , maxy: 2148267.30328144].plot(
marker='+', color='red', markersize=15, ax=base)
riversRusia_clipped.cx[minx:19217871.01839928 , maxy: 2348267.30328144].plot(edgecolor='blue', linewidth=2,
ax=base)
Out[ ]:
<Axes: >
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set(rusia_6078.geom_type), set(citiesRusia_clipped.geom_type), set(riversRusia_clipped.geom_type)
Out[ ]:
({'MultiPolygon'}, {'Point'}, {'LineString', 'MultiLineString'})
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#PROBLEMA 2:
Link_rusia="https://github.com/Halabbi/introgeodf2/raw/refs/heads/main/rusiaMAPAS_6078%20(1).gpkg"
import geopandas as gpd
from fiona import listlayers
listlayers(Link_rusia)
Out[ ]:
['country', 'cities', 'rivers', 'airports', 'rusia_states', 'rusia_municipalities']
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rusia_municipalities=gpd.read_file(Link_rusia,layer='rusia_municipalities')
rusia_municipalities.plot(facecolor='lightgrey', edgecolor='black',linewidth=0.2)
Out[ ]:
<Axes: >
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rusia_municipalities.head()
Out[ ]:
| ADM0_EN | ADM0_PCODE | ADM1_EN | ADM1_PCODE | ADM2_EN | ADM2_PCODE | UNHCR_code | geometry | |
|---|---|---|---|---|---|---|---|---|
| 0 | Russian Federation | RU | Ural Federal District | RU007 | Chelyabinsk | RU007001 | 20RUS007001 | MULTIPOLYGON Z (((14115166.204 1222034.471 0, ... |
| 1 | Russian Federation | RU | North Caucasus federal district | RU003 | Ingush | RU003003 | 20RUS003003 | MULTIPOLYGON Z (((12235424.873 502594.853 0, 1... |
| 2 | Russian Federation | RU | South federal district | RU006 | Adygey | RU006001 | 20RUS006001 | MULTIPOLYGON Z (((12003208.011 962837.238 0, 1... |
| 3 | Russian Federation | RU | Volga Federal District | RU008 | Bashkortostan | RU008001 | 20RUS008001 | MULTIPOLYGON Z (((13736990.21 1403699.147 0, 1... |
| 4 | Russian Federation | RU | South federal district | RU006 | Astrakhan' | RU006002 | 20RUS006002 | MULTIPOLYGON Z (((12729057.298 922575.797 0, 1... |
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# numero de municipalidades
len(rusia_municipalities.ADM2_EN)
Out[ ]:
83
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# numero de distritos federales
len(set(rusia_municipalities.ADM1_EN))
Out[ ]:
8
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#municipalidades del distrito federal Ural
rusia_municipalities[rusia_municipalities.ADM1_EN=='Ural Federal District'].plot()
Out[ ]:
<Axes: >
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#creamos una geometría única para el distrito federal de los Urales al unir todos sus municipios
UralFederalDistrict_union=rusia_municipalities[rusia_municipalities.ADM1_EN=='Ural Federal District'].union_all()
UralFederalDistrict_union
Out[ ]:
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#corroboramos que la union se dio correctamente
type(UralFederalDistrict_union)
Out[ ]:
shapely.geometry.multipolygon.MultiPolygon
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#distrito federal Ural unificado
gpd.GeoDataFrame(index=[0],data={'ADM':'Ural Federal District'},
crs=rusia_municipalities.crs,
geometry=[UralFederalDistrict_union])
Out[ ]:
| ADM | geometry | |
|---|---|---|
| 0 | Ural Federal District | MULTIPOLYGON Z (((14535322.461 940591.219 0, 1... |
In [ ]:
#Podemos obtener lo mismo usando dissolve
rusia_municipalities[rusia_municipalities.ADM1_EN=='Ural Federal District'].dissolve().plot()
Out[ ]:
<Axes: >
In [ ]:
UralFederalDistrict_dissolve=rusia_municipalities[rusia_municipalities.ADM1_EN=='Ural Federal District'].dissolve()
type(UralFederalDistrict_dissolve)
Out[ ]:
geopandas.geodataframe.GeoDataFrame
def __init__(data=None, *args, geometry=None, crs=None, **kwargs)
A GeoDataFrame object is a pandas.DataFrame that has one or more columns containing geometry. In addition to the standard DataFrame constructor arguments, GeoDataFrame also accepts the following keyword arguments: Parameters ---------- crs : value (optional) Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() <pyproj.crs.CRS.from_user_input>`, such as an authority string (eg "EPSG:4326") or a WKT string. geometry : str or array-like (optional) Value to use as the active geometry column. If str, treated as column name to use. If array-like, it will be added as new column named 'geometry' on the GeoDataFrame and set as the active geometry column. Note that if ``geometry`` is a (Geo)Series with a name, the name will not be used, a column named "geometry" will still be added. To preserve the name, you can use :meth:`~GeoDataFrame.rename_geometry` to update the geometry column name. Examples -------- Constructing GeoDataFrame from a dictionary. >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf col1 geometry 0 name1 POINT (1 2) 1 name2 POINT (2 1) Notice that the inferred dtype of 'geometry' columns is geometry. >>> gdf.dtypes col1 object geometry geometry dtype: object Constructing GeoDataFrame from a pandas DataFrame with a column of WKT geometries: >>> import pandas as pd >>> d = {'col1': ['name1', 'name2'], 'wkt': ['POINT (1 2)', 'POINT (2 1)']} >>> df = pd.DataFrame(d) >>> gs = geopandas.GeoSeries.from_wkt(df['wkt']) >>> gdf = geopandas.GeoDataFrame(df, geometry=gs, crs="EPSG:4326") >>> gdf col1 wkt geometry 0 name1 POINT (1 2) POINT (1 2) 1 name2 POINT (2 1) POINT (2 1) See also -------- GeoSeries : Series object designed to store shapely geometry objects
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# Verificar el resultado
UralFederalDistrict_dissolve
Out[ ]:
| geometry | ADM0_EN | ADM0_PCODE | ADM1_EN | ADM1_PCODE | ADM2_EN | ADM2_PCODE | UNHCR_code | |
|---|---|---|---|---|---|---|---|---|
| 0 | MULTIPOLYGON Z (((14535322.461 940591.219 0, 1... | Russian Federation | RU | Ural Federal District | RU007 | Chelyabinsk | RU007001 | 20RUS007001 |
In [ ]:
#eliminamos las columnas que no son necesarias
UralFederalDistrict_dissolve.drop(columns=['ADM2_EN', 'ADM2_PCODE', 'UNHCR_code'], inplace=True)
# obtenemos
UralFederalDistrict_dissolve
Out[ ]:
| geometry | ADM0_EN | ADM0_PCODE | ADM1_EN | ADM1_PCODE | |
|---|---|---|---|---|---|
| 0 | MULTIPOLYGON Z (((14535322.461 940591.219 0, 1... | Russian Federation | RU | Ural Federal District | RU007 |
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#usando dissolve también para disolver las municipalidades de todos los distritos federales de rusia
rusia_municipalities.dissolve(by='ADM1_EN').plot(facecolor='yellow', edgecolor='black',linewidth=0.2)
Out[ ]:
<Axes: >
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#obtenemos un solo polígono para cada región de Rusia, en lugar de múltiples polígonos de cada municipio.
Rusia_adm1_diss=rusia_municipalities.dissolve(by='ADM1_EN')
Rusia_adm1_diss
In [ ]:
#eliminamos las columnas innesarias y obtenemos la info sobre Rusia_adm1_diss
Rusia_adm1_diss.drop(columns=['ADM2_EN','ADM2_PCODE','UNHCR_code'],inplace=True)
Rusia_adm1_diss.reset_index(inplace=True)
Rusia_adm1_diss.info()
<class 'geopandas.geodataframe.GeoDataFrame'> RangeIndex: 8 entries, 0 to 7 Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ADM1_EN 8 non-null object 1 geometry 8 non-null geometry 2 ADM0_EN 8 non-null object 3 ADM0_PCODE 8 non-null object 4 ADM1_PCODE 8 non-null object dtypes: geometry(1), object(4) memory usage: 448.0+ bytes
EJERCICIO 3:
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#Agregamos a linkworldMAP la capa indicators
linkWorldMAP="https://github.com/CienciaDeDatosEspacial/intro_geodataframe/raw/main/maps/worldMaps.gpkg"
import geopandas as gpd
from fiona import listlayers
listlayers(linkWorldMAP)
indicators=gpd.read_file(linkWorldMAP,layer='indicators')
In [ ]:
indicators.head()
Out[ ]:
| COUNTRY | Officialstatename | InternetccTLD | iso2 | iso3 | fragility_date | fragility | co2 | co2_date | region | ForestRev_gdp | ForestRev_date | fragility_Qt | fragility_Qt_jc5 | fragility_Qt_jc5_cat | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ANTIGUA AND BARBUDA | Antigua and Barbuda | .ag | AG | ATG | 2019 | 54.4 | 729000.0 | 2019 | CENTRAL AMERICA AND THE CARIBBEAN | 0.00 | 2018 | -0.530113 | 1 | 1_Good | MULTIPOLYGON (((-61.73889 17.54055, -61.75195 ... |
| 1 | AFGHANISTAN | The Islamic Republic of Afghanistan | .af | AF | AFG | 2019 | 105.0 | 7893000.0 | 2019 | SOUTH ASIA | 0.20 | 2018 | 1.827146 | 4 | 4_Poor | POLYGON ((61.27656 35.60725, 61.29638 35.62853... |
| 2 | ALGERIA | The People's Democratic Republic of Algeria | .dz | DZ | DZA | 2019 | 75.4 | 151633000.0 | 2019 | AFRICA | 0.10 | 2018 | 0.304520 | 3 | 3_Bad | POLYGON ((-5.15213 30.18047, -5.13917 30.19236... |
| 3 | AZERBAIJAN | The Republic of Azerbaijan | .az | AZ | AZE | 2019 | 73.2 | 35389000.0 | 2019 | MIDDLE EAST | 0.02 | 2018 | 0.233695 | 2 | 2_Middle | MULTIPOLYGON (((46.54037 38.87559, 46.49554 38... |
| 4 | ALBANIA | The Republic of Albania | .al | AL | ALB | 2019 | 58.9 | 3794000.0 | 2019 | EUROPE | 0.18 | 2018 | -0.424447 | 1 | 1_Good | POLYGON ((20.79192 40.43154, 20.78722 40.39472... |
In [ ]:
#ahora disolvemos los datos por región con la aggfunc
#agrupa las filas de indicators por la columna region, combinando geometrías
#en aggfunc agrega valores: agrupa el promedio de country, fragility, co2 y forestrev_gdp
indicators.dissolve(
by="region",
aggfunc={
"COUNTRY": "count",
"fragility": ["mean"],
"co2": ["mean"],
"ForestRev_gdp": ["mean"]
},as_index=False,
)
Out[ ]:
| region | geometry | (COUNTRY, count) | (fragility, mean) | (co2, mean) | (ForestRev_gdp, mean) | |
|---|---|---|---|---|---|---|
| 0 | AFRICA | MULTIPOLYGON (((-2.92813 5.10022, -2.93222 5.1... | 51 | 85.664706 | 1.670835e+07 | 3.239804 |
| 1 | AUSTRALIA AND OCEANIA | MULTIPOLYGON (((-176.51779 -43.90778, -176.532... | 6 | 55.100000 | 7.679500e+07 | 3.630000 |
| 2 | CENTRAL AMERICA AND THE CARIBBEAN | MULTIPOLYGON (((-81.71556 7.44681, -81.71362 7... | 16 | 62.993750 | 1.104856e+07 | 0.359375 |
| 3 | CENTRAL ASIA | MULTIPOLYGON (((50.03472 45.04833, 50.0425 45.... | 6 | 72.883333 | 3.898807e+08 | 0.236667 |
| 4 | EAST AND SOUTHEAST ASIA | MULTIPOLYGON (((105.26361 -6.54472, 105.26138 ... | 15 | 64.120000 | 9.608337e+08 | 0.586667 |
| 5 | EUROPE | MULTIPOLYGON (((-18.13223 27.77264, -18.1132 2... | 39 | 40.348718 | 9.866674e+07 | 0.199231 |
| 6 | MIDDLE EAST | MULTIPOLYGON (((42.76819 14.06653, 42.7868 14.... | 16 | 71.756250 | 1.583754e+08 | 0.032500 |
| 7 | NORTH AMERICA | MULTIPOLYGON (((-155.211 20.00166, -155.18335 ... | 3 | 42.566667 | 2.073395e+09 | 0.073333 |
| 8 | SOUTH AMERICA | MULTIPOLYGON (((-74.93431 -51.85501, -74.93251... | 11 | 61.436364 | 8.613718e+07 | 1.064545 |
| 9 | SOUTH ASIA | MULTIPOLYGON (((73.10193 -0.58431, 73.09915 -0... | 8 | 83.975000 | 3.309434e+08 | 0.365000 |
In [ ]:
#ahora almacenamos el resultado en indicadores_por_region
#visualizamos con plot
indicadores_por_region=indicators.dissolve(
by="region",
aggfunc={
"COUNTRY": "count",
"fragility": ["mean"],
"co2": ["mean"],
"ForestRev_gdp": ["mean"]
},as_index=False,
)
indicadores_por_region.plot(column = 'region')
Out[ ]:
<Axes: >
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!pip install mapclassify
Collecting mapclassify Downloading mapclassify-2.8.1-py3-none-any.whl.metadata (2.8 kB) Requirement already satisfied: networkx>=2.7 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (3.4.2) Requirement already satisfied: numpy>=1.23 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (1.26.4) Requirement already satisfied: pandas!=1.5.0,>=1.4 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (2.2.2) Requirement already satisfied: scikit-learn>=1.0 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (1.5.2) Requirement already satisfied: scipy>=1.8 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (1.13.1) Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas!=1.5.0,>=1.4->mapclassify) (2.8.2) Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas!=1.5.0,>=1.4->mapclassify) (2024.2) Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas!=1.5.0,>=1.4->mapclassify) (2024.2) Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->mapclassify) (1.4.2) Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->mapclassify) (3.5.0) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas!=1.5.0,>=1.4->mapclassify) (1.16.0) Downloading mapclassify-2.8.1-py3-none-any.whl (59 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 59.1/59.1 kB 2.1 MB/s eta 0:00:00 Installing collected packages: mapclassify Successfully installed mapclassify-2.8.1
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#para visualizar mejor el indicador de fragilidad con una paleta de color
indicadores_por_region.plot(column =('fragility', 'mean'),scheme='quantiles', cmap='OrRd',
legend=True,
legend_kwds={"title": "Fragility",'loc': 'lower left'},
edgecolor='black',linewidth=0.2,
figsize=(15, 10))
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<Axes: >
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rusia_6078.centroid
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| 0 | |
|---|---|
| 188 | POINT (16052701.159 2148267.303) |
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#calculamos las coordenadas del centroide de rusia
rusia_6078.centroid.x.values[0],rusia_6078.centroid.y.values[0]
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(16052701.159460908, 2148267.3032814446)
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#reemplazamos type por kind porque en la data que tenemos
#hay una columna type, esto nos puede causar problemas en el siguiente código,por eso el cambio
airports_Rusia.rename(columns={'type': 'kind'}, inplace=True)
airports_Rusia.head()
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| name | kind | latitude_deg | longitude_deg | elevation_ft | iso_region | municipality | geometry | |
|---|---|---|---|---|---|---|---|---|
| 0 | Dalnegorsk Airport | small_airport | 44.558748 | 135.490005 | 833.0 | RU-PRI | Dalnegorsk | POINT (19607662.474 1506754.038) |
| 1 | Dalnerechensk Airport | small_airport | 45.877949 | 133.735822 | 272.0 | RU-PRI | Dalnerechensk | POINT (19381620.39 1491586.152) |
| 2 | Deputatskiy Airport | small_airport | 69.392503 | 139.890012 | 950.0 | RU-SA | Deputatskiy | POINT (17418835.504 3472608.835) |
| 3 | Levashovo Air Base | medium_airport | 60.086700 | 30.193300 | 60.0 | RU-SPE | St. Petersburg | POINT (12842271.541 2684604.065) |
| 4 | Afrikanda Air Base | closed | 67.456703 | 32.786701 | 505.0 | RU-MUR | Afrikanda | POINT (13576772.441 3119529.771) |
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# Definimos las coordenadas del centroide
centroidX,centroidY=rusia_6078.centroid.x.values[0],rusia_6078.centroid.y.values[0]
# Segmentamos los aeropuertos de Rusia en cuadrantes
Rusia_AirTopLeft=airports_Rusia[airports_Rusia.kind=='medium_airport'].cx[:centroidX,centroidY:]
Rusia_AirTopRight=airports_Rusia[airports_Rusia.kind=='medium_airport'].cx[centroidX:,centroidY:]
Rusia_AirBottomLeft=airports_Rusia[airports_Rusia.kind=='medium_airport'].cx[:centroidX,:centroidY]
Rusia_AirBottomRight=airports_Rusia[airports_Rusia.kind=='medium_airport'].cx[centroidX:,:centroidY]
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#graficamos los aeropuertos por cuadrante
base=Rusia_AirTopLeft.plot(facecolor='grey', alpha=0.4)
Rusia_AirTopRight.plot(ax=base,facecolor='orange', alpha=0.4)
Rusia_AirBottomLeft.plot(ax=base,facecolor='green', alpha=0.4)
Rusia_AirBottomRight.plot(ax=base,facecolor='red', alpha=0.4)
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<Axes: >
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Rusia_AirBottomLeft
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| name | kind | latitude_deg | longitude_deg | elevation_ft | iso_region | municipality | geometry | |
|---|---|---|---|---|---|---|---|---|
| 12 | Balashov Air Base | medium_airport | 51.533298 | 43.299999 | 568.0 | RU-SAR | Balashov | POINT (12739700.429 1360162.599) |
| 16 | Borisoglebsk Air Base | medium_airport | 51.366699 | 42.178299 | 390.0 | RU-VOR | Borisoglebsk | POINT (12661688.435 1398398.108) |
| 20 | Buturlinovka Air Base | medium_airport | 50.792210 | 40.606155 | 505.0 | RU-VOR | Buturlinovka | POINT (12525743.518 1424049.446) |
| 37 | Grozny Airport | medium_airport | 43.388106 | 45.699772 | 548.0 | RU-CE | Grozny | POINT (12283048.407 442114.017) |
| 45 | Mius Airport | medium_airport | 54.958332 | 43.311668 | 525.0 | RU-NIZ | Sarov | POINT (12997487.012 1678016.971) |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1656 | Balakovo Airport | medium_airport | 51.858299 | 47.745602 | 95.0 | RU-SAR | Balakovo | POINT (13029644.38 1192456.728) |
| 1668 | Buguruslan Severny Airport | medium_airport | 53.718399 | 52.371899 | 728.0 | RU-ORE | Buguruslan | POINT (13432430.569 1201136.517) |
| 1670 | Bezymyanka Airfield | medium_airport | 53.220001 | 50.325001 | 135.0 | RU-SAM | Samara | POINT (13276886.07 1225331.901) |
| 1679 | Lebyazhye Air Base | medium_airport | 50.201698 | 45.208302 | 381.0 | RU-VGG | Kamyshin | POINT (12755670.107 1143286.445) |
| 1682 | Rtishchevo Air Base | medium_airport | 52.297317 | 43.724100 | 673.0 | RU-SAR | Rtishchevo | POINT (12821812.306 1412132.581) |
120 rows × 8 columns
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#acá todavía no se puede hacer una envolvente convexa
Rusia_AirBottomLeft.convex_hull.plot()
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<Axes: >
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#tenemos que Disolver y crear una envokvente de ángulos convexos
Rusia_AirBottomLeft.dissolve().convex_hull.plot()
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<Axes: >
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#acá observamos que es un polígono
Rusia_AirBottomLeft.dissolve().convex_hull
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| 0 | |
|---|---|
| 0 | POLYGON ((12388442.758 277400.66, 12188335.559... |
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#si nuestros polígonos no están previamente combinados, se usa la envolvente convexa
#al aplicar convex_hull se tiene una envolvente convexa individual para cada municipalidad
rusia_municipalities.cx[:centroidX,:centroidY].convex_hull.plot(edgecolor='red')
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<Axes: >
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#los large_airport se combinan en una sola geometría con union_all
large_airport=airports_Rusia[airports_Rusia.kind=='large_airport']
large_airport.union_all()
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#con convex_hull obtenemos una única envolvente convexa
large_airport.union_all().convex_hull
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#convertimos la geoserie a un geodataframe
LargeAirport_hull= gpd.GeoDataFrame(index=[0],
crs=large_airport.crs,
geometry=[large_airport.union_all().convex_hull])
LargeAirport_hull
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| geometry | |
|---|---|
| 0 | POLYGON ((11878396.961 784544.53, 12818681.622... |
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#ploteamos
base=rusia_6078.plot(facecolor='yellow')
large_airport.plot(ax=base)
LargeAirport_hull.plot(ax=base,facecolor='green',
edgecolor='white',alpha=0.4,
hatch='X')
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<Axes: >
EJERCICIO 4:
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# Norte
MunisN_rusia=rusia_municipalities.cx[:,centroidY:]
# Sur
MunisS_rusia=rusia_municipalities.cx[:,:centroidY]
# Oeste
MunisW_rusia=rusia_municipalities.cx[:centroidX,:]
# Este
MunisE_rusia=rusia_municipalities.cx[centroidX:,:]
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base=MunisN_rusia.plot(facecolor='yellow', edgecolor='black',linewidth=0.2, alpha=0.6)
MunisS_rusia.plot(facecolor='grey', edgecolor='black',linewidth=0.2,ax=base, alpha=0.4)
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<Axes: >
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#Gráfico de la superposición
base=MunisE_rusia.plot(facecolor='yellow', edgecolor='black',linewidth=0.2, alpha=0.6)
MunisW_rusia.plot(facecolor='grey', edgecolor='black',linewidth=0.2,ax=base, alpha=0.4)
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<Axes: >
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# mantenemos lo que es común en GDF
munisNS_rusia=MunisN_rusia.overlay(MunisS_rusia, how="intersection",keep_geom_type=True)
munisNS_rusia.plot()
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<Axes: >
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MunisN_rusia.sjoin(MunisS_rusia, how="inner", predicate='contains').plot()
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<Axes: >
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# vamos a mantener la superposición
munisWE_rusia=MunisW_rusia.overlay(MunisE_rusia, how="intersection",keep_geom_type=True)
munisWE_rusia.plot(edgecolor='white',linewidth=0.1)
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<Axes: >
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#Unión de los dos GeoDF anteriores
munisNS_rusia.info()
<class 'geopandas.geodataframe.GeoDataFrame'> RangeIndex: 15 entries, 0 to 14 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ADM0_EN_1 15 non-null object 1 ADM0_PCODE_1 15 non-null object 2 ADM1_EN_1 15 non-null object 3 ADM1_PCODE_1 15 non-null object 4 ADM2_EN_1 15 non-null object 5 ADM2_PCODE_1 15 non-null object 6 UNHCR_code_1 15 non-null object 7 ADM0_EN_2 15 non-null object 8 ADM0_PCODE_2 15 non-null object 9 ADM1_EN_2 15 non-null object 10 ADM1_PCODE_2 15 non-null object 11 ADM2_EN_2 15 non-null object 12 ADM2_PCODE_2 15 non-null object 13 UNHCR_code_2 15 non-null object 14 geometry 15 non-null geometry dtypes: geometry(1), object(14) memory usage: 1.9+ KB
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munisWE_rusia.info()
<class 'geopandas.geodataframe.GeoDataFrame'> RangeIndex: 3 entries, 0 to 2 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ADM0_EN_1 3 non-null object 1 ADM0_PCODE_1 3 non-null object 2 ADM1_EN_1 3 non-null object 3 ADM1_PCODE_1 3 non-null object 4 ADM2_EN_1 3 non-null object 5 ADM2_PCODE_1 3 non-null object 6 UNHCR_code_1 3 non-null object 7 ADM0_EN_2 3 non-null object 8 ADM0_PCODE_2 3 non-null object 9 ADM1_EN_2 3 non-null object 10 ADM1_PCODE_2 3 non-null object 11 ADM2_EN_2 3 non-null object 12 ADM2_PCODE_2 3 non-null object 13 UNHCR_code_2 3 non-null object 14 geometry 3 non-null geometry dtypes: geometry(1), object(14) memory usage: 488.0+ bytes
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#Combinanción de las geometrías
keep=['ADM0_EN_1','ADM1_EN_1','ADM2_EN_1','geometry']
munisNS_rusia=munisNS_rusia.loc[:,keep]
munisWE_rusia=munisWE_rusia.loc[:,keep]
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# Las geometrías están correctas
munisNS_rusia.overlay(munisWE_rusia,how="union",keep_geom_type=True)
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| ADM0_EN_1_1 | ADM1_EN_1_1 | ADM2_EN_1_1 | ADM0_EN_1_2 | ADM1_EN_1_2 | ADM2_EN_1_2 | geometry | |
|---|---|---|---|---|---|---|---|
| 0 | Russian Federation | Siberian Federal District | Krasnoyarsk | Russian Federation | Siberian Federal District | Krasnoyarsk | MULTIPOLYGON Z (((16286187.943 3215981.232 0, ... |
| 1 | Russian Federation | Ural Federal District | Yamal-Nenets | NaN | NaN | NaN | MULTIPOLYGON Z (((14815559.768 2524399.669 0, ... |
| 2 | Russian Federation | Far Eastern Federal District | Sakha | NaN | NaN | NaN | MULTIPOLYGON Z (((16456624.732 3095964.681 0, ... |
| 3 | Russian Federation | Central Federal District | Yaroslavl' | NaN | NaN | NaN | POLYGON Z ((13098775.326 2206672.261 0, 131035... |
| 4 | Russian Federation | Northwestern Federal District | Vologda | NaN | NaN | NaN | POLYGON Z ((13234016.536 2505178.892 0, 132364... |
| 5 | Russian Federation | Central Federal District | Moskva | NaN | NaN | NaN | POLYGON Z ((12878179.222 2095526.89 0, 1287818... |
| 6 | Russian Federation | Far Eastern Federal District | Khabarovsk | NaN | NaN | NaN | MULTIPOLYGON Z (((18105966.229 3078452.168 0, ... |
| 7 | Russian Federation | Northwestern Federal District | Komi | NaN | NaN | NaN | POLYGON Z ((14770560.909 2453741.766 0, 147741... |
| 8 | Russian Federation | Ural Federal District | Khanty-Mansiy | NaN | NaN | NaN | POLYGON Z ((14539359.51 2111243.737 0, 1454010... |
| 9 | Russian Federation | Siberian Federal District | Irkutsk | NaN | NaN | NaN | POLYGON Z ((16735570.822 2135598.667 0, 167343... |
| 10 | Russian Federation | Northwestern Federal District | Arkhangel'sk | NaN | NaN | NaN | MULTIPOLYGON Z (((13801449.763 2748246.167 0, ... |
| 11 | Russian Federation | Far Eastern Federal District | Amur | NaN | NaN | NaN | POLYGON Z ((17749757.38 1792115.95 0, 17754875... |
| 12 | Russian Federation | Far Eastern Federal District | Sakhalin | NaN | NaN | NaN | MULTIPOLYGON Z (((18952378.764 2665985.48 0, 1... |
| 13 | Russian Federation | Central Federal District | Tver' | NaN | NaN | NaN | POLYGON Z ((13005107.154 2279013.435 0, 130054... |
| 14 | Russian Federation | Central Federal District | Smolensk | NaN | NaN | NaN | POLYGON Z ((12632796.548 2194094.387 0, 126364... |
| 15 | NaN | NaN | NaN | Russian Federation | Siberian Federal District | Tuva | POLYGON Z ((16505543.869 751032.031 0, 1650958... |
| 16 | NaN | NaN | NaN | Russian Federation | Siberian Federal District | Khakass | MULTIPOLYGON Z (((15891704.229 859395.4 0, 158... |
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import pandas as pd
pd.concat([munisNS_rusia,munisWE_rusia],ignore_index=True)
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| ADM0_EN_1 | ADM1_EN_1 | ADM2_EN_1 | geometry | |
|---|---|---|---|---|
| 0 | Russian Federation | Ural Federal District | Yamal-Nenets | MULTIPOLYGON Z (((14815640.739 2524375.07 0, 1... |
| 1 | Russian Federation | Far Eastern Federal District | Sakha | MULTIPOLYGON Z (((16456564.304 3095930.299 0, ... |
| 2 | Russian Federation | Siberian Federal District | Krasnoyarsk | MULTIPOLYGON Z (((16286244.056 3216009.491 0, ... |
| 3 | Russian Federation | Central Federal District | Yaroslavl' | POLYGON Z ((13098775.326 2206672.261 0, 131035... |
| 4 | Russian Federation | Northwestern Federal District | Vologda | POLYGON Z ((13234016.536 2505178.892 0, 132364... |
| 5 | Russian Federation | Central Federal District | Moskva | POLYGON Z ((12878179.222 2095526.89 0, 1287818... |
| 6 | Russian Federation | Far Eastern Federal District | Khabarovsk | MULTIPOLYGON Z (((18105966.229 3078452.168 0, ... |
| 7 | Russian Federation | Northwestern Federal District | Komi | POLYGON Z ((14770560.909 2453741.766 0, 147741... |
| 8 | Russian Federation | Ural Federal District | Khanty-Mansiy | POLYGON Z ((14538141.004 2114423.131 0, 145393... |
| 9 | Russian Federation | Siberian Federal District | Irkutsk | POLYGON Z ((16729408.032 2141372.466 0, 167339... |
| 10 | Russian Federation | Northwestern Federal District | Arkhangel'sk | MULTIPOLYGON Z (((13801449.763 2748246.167 0, ... |
| 11 | Russian Federation | Far Eastern Federal District | Amur | POLYGON Z ((17749757.38 1792115.95 0, 17754875... |
| 12 | Russian Federation | Far Eastern Federal District | Sakhalin | MULTIPOLYGON Z (((18952378.764 2665985.48 0, 1... |
| 13 | Russian Federation | Central Federal District | Tver' | POLYGON Z ((13005107.154 2279013.435 0, 130054... |
| 14 | Russian Federation | Central Federal District | Smolensk | POLYGON Z ((12632796.548 2194094.387 0, 126364... |
| 15 | Russian Federation | Siberian Federal District | Krasnoyarsk | MULTIPOLYGON Z (((16286244.056 3216009.491 0, ... |
| 16 | Russian Federation | Siberian Federal District | Tuva | POLYGON Z ((16486705.841 752820.945 0, 1649772... |
| 17 | Russian Federation | Siberian Federal District | Khakass | MULTIPOLYGON Z (((15887559.358 858579.158 0, 1... |
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munisNS_rusia.dissolve().overlay(munisWE_rusia.dissolve(), how="union",keep_geom_type=True).dissolve().plot()
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<Axes: >
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#Guarda el archivo
muniMidRusia=munisNS_rusia.dissolve().overlay(munisWE_rusia.dissolve(), how="union",keep_geom_type=True).dissolve()
muniMidRusia
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# Limpieza del código
muniMidRusia['zone']='middles'
muniMidRusia=muniMidRusia.loc[:,['ADM0_EN_1_1','zone','geometry']]
muniMidRusia
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#Diferencias con los municipios
rusia_municipalities.overlay(muniMidRusia, how='difference').plot()
/usr/local/lib/python3.10/dist-packages/geopandas/geodataframe.py:2675: UserWarning: `keep_geom_type=True` in overlay resulted in 79 dropped geometries of different geometry types than df1 has. Set `keep_geom_type=False` to retain all geometries return geopandas.overlay(
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<Axes: >
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#Diferencia simétrica
MunisN_rusia.overlay(MunisS_rusia, how="symmetric_difference",keep_geom_type=False).plot()
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<Axes: >
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#Se conserva lo que no está en la intersección
MunisW_rusia.overlay(MunisE_rusia, how="symmetric_difference",keep_geom_type=False).plot()
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<Axes: >
